用AI整合微生物组与临床数据，提升阿尔茨海默病检测准确性
ADAM: An AI Reasoning and Bioinformatics Model for Alzheimer's Disease Detection and Microbiome-Clinical Data Integration
- 构建多智能体LLM框架，融合微生物组、临床数据与文献证据
- 相比XGBoost，F1均值更高且方差更小，结果更稳定可靠
- 适合神经科学与生物信息学研究者，推动疾病诊断智能化
阿尔茨海默病分析模型（ADAM）是一种多智能体推理大语言模型框架，用于整合并分析多模态数据，包括微生物组谱、临床数据集及外部知识库，以增强对阿尔茨海默病（AD）的理解与分类。通过利用具备代理能力的大语言模型，ADAM从多元数据源中生成洞察，并结合文献驱动的证据进行上下文化解释。与XGBoost的对比评估显示，ADAM在平均F1得分上显著提升，方差显著降低，展现出更强的鲁棒性与一致性，尤其在使用人类生物数据时表现突出。尽管当前主要针对二分类任务和两种数据模态，未来版本将引入神经影像和外周生物标志物等更多数据类型，并拓展至疾病进展预测，从而提升其在阿尔茨海默病研究与诊断应用中的可扩展性与适用性。
原文摘要 · Abstract (English)
Alzheimer's Disease Analysis Model (ADAM) is a multi-agent reasoning large language model (LLM) framework designed to integrate and analyze multimodal data, including microbiome profiles, clinical datasets, and external knowledge bases, to enhance the understanding and classification of Alzheimer's disease (AD). By leveraging the agentic system with LLM, ADAM produces insights from diverse data sources and contextualizes the findings with literature-driven evidence. A comparative evaluation with XGBoost revealed a significantly improved mean F1 score and significantly reduced variance for ADAM, highlighting its robustness and consistency, particularly when utilizing human biological data. Although currently tailored for binary classification tasks with two data modalities, future iterations will aim to incorporate additional data types, such as neuroimaging and peripheral biomarkers, and expand them to predict disease progression, thereby broadening ADAM's scalability and applicability in AD research and diagnostic applications.
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